Reinterpreting The Cortical Circuit
Hiroshi Yamakawa *1, *3, Naoya Arakawa *1, *3, Koichi Takahashi *2, *3, *4*1 Dwango Co., Ltd., *2 RIKEN Quantitative Biology Center, *3 The Whole Brain Architecture
Initiative, *4 Keio University Graduate School of Media and Governance

Abstract
As various functions are accomplished with auniform mechanism of the micro-circuit in the
neocortex (i.e., the canonical cortical circuit), thissystem will yield clues about general intelligence.
Though understanding the mechanism of the entire
brain and its circuits would serve the creation of
artificial general intelligence, its required
specifications have not been clarified. In this paper,
we present a framework for the canonical circuit,defining its functions and interface semantics while
integrating models in neuroscience. In addition,
candidate models with artificial neural networks
are evaluated with the specifications.

1

Introduction

The whole brain architecture approach is a research
approach aiming at engineering human-like artificial general
intelligence (AGI) learning from the entire architecture of
the brain, where major brain organs such as the neocortex,
basal ganglia, hippocampus, amygdala, thalamus, andcerebellum are implemented as machine learning modules
that are connected based on neuroscientific findings such as
mesoscopic connectomes.
The approach aims to build AGI that acquires the ability tosolve various problems through learning. The neural circuitof the neocortex has a uniform structure, but can exhibit
various functions according to the nature of the input andoutput. Thus, the neocortex is considered to be the brainorgan for versatility. In neuroscience, studies on the
canonical cortical circuit/model of the neocortex have been
carried out [Harris and Mrsic-Flogel, 2013]. We call the
hypothetical general machine learning algorithm of the
neocortical circuit the Neocortical Master Algorithm (NMA
hereafter), named after the appellation for the ultimate
learning machine introduced in [Domingos, 2015].
In this paper, we identify the input and output that are
indispensable for NMA based on knowledge inneuroscience and machine learning, list machine learningalgorithms related to NMA, and discuss its I/O and required
functions.
Here, we assume cognitive architecture to operate in realtime, as it is the case in the biological brain.

2

The NMA framework

With the advent of artificial neural networks (ANN) basedon deep learning, there are an increasing number of systems
that can realize various human cognitive abilities. These
examples suggest that NMA could be realized by combining
ANNs, such as the convolutional neural network (CNN)
capable of information compression and pooling, LSTM
with a gating mechanism, and reinforcement learning.

2.1 Reviewing the interface of NMA
What we propose here as the NMA framework are the I/Osemantics for the canonical cortical circuit. In humans, theneocortex can be decomposed into functional regions, suchas the primary visual cortex (V1) and supplementary motor
cortex (SMA). Those regions correspond to NMA units;thus, it is important to determine the semantics of their input
and output.
While the real neocortex forms a six-layer structure, here we
regard it to have the five layers {L1, L2/3, L4, L5, L6} inour model. The semantics of the input and output of NMA isdetermined as follows. As for output signals, as a specificsubtype of neurons, output signals and output semantics
depend on the layer to which the subtype belongs. As for
input signals, they need to be classified according to thenature of the brain organs that transmit them, as theintegration of signals with multiple semantics may takeplace.
In the following, we integrate and merge I/O semantics fromfour neuroscientific sources: the Bayesian filter hypothesis[Funamizu et al., 2016], the canonical neocortex circuit by
AGI.io [Kowadlo and Rawlinson, 2015] [Kowadlo and
Rawlinson, 2016] the Cognitive Consilience [Solari andStoner, 2011], and the interlaminar relationship of the
neocortex [Hawkins, 2010], as shown in Fig. 1.

2.2 I/O Semantics of NMA
Here, signals are classified into state signals and control
signals, where the former represent a belief in the current
situation, corresponding to sensor information obtained
from the external world in real time. State signals are theoutput from L2/3 (Output 3 in Fig.1) to L2/3 in othercorteces, L4 in the higher cortex, and hippocampus. The

Figure 1: Framework of Neocortical Master Algorithm"hidden state" signal is a signal with temporal information,
including a history of a certain state and prediction. It is theoutput from L5 (Output 2) and is transmitted to the basal
ganglia and thalamus. The "action output" signal is theoutput from another subtype of neurons in L5 to thepyramidal tract.
Of the input signals, those related to states are classified intothe "state (bottom-up)" signal (Input 3) received from thelower cortex, and the "state (top-down)" signal (Input 4)
received from the higher cortex. The state (top-down) signal
obtained from L2/3 of the higher cortex and hippocampus ismainly the input to L2/3 and further to L6. The state(bottom-up) signal (Input 4) obtained from L2/3 of thelower cortex is the input to L4 or L2/3. The hidden statesignal (Input 5) originating from L5 in the lower cortex isprojected (transmitted) through the thalamus into L4. Here,note that the hidden state output from L5 carrying theinformation of the past is not directly sent to the lower
cortex.
The "control" signal is the signal related to attention and thecontext outputted from L6 (Output 1) and transmitted to L6
or L1 in the lower cortex. The control signal obtained from
L6 in the higher cortex or from the thalamus via the basal
ganglia is the input to L1 or L6 (Input 1). The control signal
obtained from the thalamus via the basal ganglia is the input
to L5 as "hidden state" control output (Input 2).
In this paper, L4 is used only for information selection, suchas pooling in visual information processing, the model ofthe external world is held in a recurrent loop between L2/3
and L5, where temporal hidden states are maintained, andthe control signal from L6 controls the activity of L5 andL2/3 via L1.

There are ample neuroscientific findings for L2/3 on therecognition pathway and computational models to
implement it. Findings have also been accumulating for thebasal ganglia loop originating in L5, often assumed torealize reinforcement learning (see the "PBWM model"
section below). As for the input and output of L6,
neuroscientific findings are still scant, and we found neithera hypothesis nor a computational model about its function.
Two types of top-down signals
The top-down signal from the higher cortex to the lower
cortex supports attention, prediction, and motion generation.
From the viewpoint of NMA, top-down signals are
categorized into two types: decoding and controlling.
The decoding signal reproduces (or predicts) representationsin the lower cortex (a generation model). In Fig. 1, there is a
top-down path that directly projects the state of the higher
L2/3 (Output 3) to the lower L2/3 as Input 3. Input 3 also
receives information from the hippocampus and cerebellum.
The hippocampus may represent (index) current (orreplayed) situations and cerebellum-simulated states. In thisregard, they seem to send contextual information to generate
representation in the cortex similar to the top-down input
from Output 3.
The control signal has two origins. The control signal istransmitted top-down as Output 1 from L6 projected to L6
or L1 in the lower cortex as Input 1. The control signal isalso generated in the basal ganglia, to which the hidden state(Output 2) from L5 is projected, and is sent to the neocortex
as Input 1 and Input 2. As this signal is merged for various
information, it cannot be classified into top-down orbottom-up.

Table 1: List of input and output and functions of existing models
$(0 +$'("'$,$.
!$%, *
('+$%%$ '
#0)(,# +$
+
2


HTM

BESOM

StackedAuto
Encoder

LadderNet

CNN

Frontalcortex for
PBWM

$ **#0

2

2

2

 

2

2

2

2

N/A

(',*(%

2

2

2

2













2

')-,

$ '+,, (-,)-,(',*(%

2

2



2













2

,, ,()(/'

2

2



2

2

2

2

2

2





,, (,,(&-)

2

2

2

2

2

2

2

2

2

2

2

$ '+,, (,,(&-)

2

2

2

2



2











(',*(%

2

2

2

2















-,)-, $ '+,,

2

2

2

2



2









2

-,)-,

(',*(%

2

2



2

2

2

2

2

2

2



$& + *$ +)* $,$('

2

2

2













2



D/J







2

2





2



2

2

2

2

2

2

2



2



-,)-,

'+-) *.$+  $+ ','"% *,#("('%$1,$(' 2% *'$'"
2
, "(*$1,$('
$& '+$('* -,$('2)*+ * )* + ',,$('

2







$',$'+$&)% +,,', *'%
$'$, +,, &#$'
+,,

2

2











2

2

2









-+#(/'-,(&,('

2













D/J: Difficlult to judge,_ : Not present,

Discussion about representation of L2/3 and L5
As intelligent agents must operate based on incomplete
information from the external world, the interpretation of
the world becomes ambiguous. However, neural activity
cannot simultaneously hold multiple states, not to mention
enuerating all possibilities. Thus, in order to grasp theexternal world, it is necessary to interpret the current input
while leaving various possibilities in view of the knowledge
at hand, which is assumed to be expressed in L2/3 of the
neocortex in our hypothesis.
Meanwhile, in order to work towards the outside world, it isnecessary to exercise consistent interpretation from
temporally spreaded viewpoints while considering theprevious input from the world. Such interpretations are
assumed to be represented in the L5 of the neocortex in our
hypothesis.
Since these two aspects are always present, L2/3 and L5
should share information in NMA. If intelligent agents only
have expressions like L2/3, they cannot form consistentintentional actions. If they only have a representation likeL5, it will take actions based on biased beliefs. They can
perform consistent actions while properly grasping theworld by combining the states expressed in the two forms.

3

PredNet')-, ')-,
 ')-, ')-,

3
:
6
4
9
5
7
6
8

+ &',$+!-',$('+  %Functions of NMA

3.1 Function list
An ideal NMA will have the following functions.

✔ Present,

N/A

TBI:To be implemented

Hierarchy
The learner is composed of similar or compatible
computational layers through which more complex patterns
are represented.
Dimension reduction
The function to approximate a high-dimensional state spacein a lower-dimensional one while avoiding large
information loss. It makes reinforcement learning efficient
by reducing the search space to realistically occurring states.
Unsupervised Learning
Ability to change internal parameters from input sequences
without teacher or reward signals. Examples include timeseries prediction, disentangling/orthogonalization, andcategorization.
When parameters (i.e., synaptic weights) in the neocortex
are determined through experience, unsupervised learning isrequired because teacher or reward signals are too sparse inreal life.
Time series prediction
Prediction is often required for solving problems andsurvival. Technically, it also includes the estimation of thehidden states behind time series. As conventional techniques,
the hidden-state markov model (HMM) and Kalman filter
are commonly used. For prediction in the brain, there is ahypothesis called predictive coding, for which prediction
error plays a key role [Rao and Ballard, 1999].

Disentangling / Orthogonalization
In a high dimensional state space, multiple factors generally
exist in the form of entanglement. It becomes apparent
especially when dimension reduction is performed.
Disentangling or orthogonalization is the function to extract
independent factors. For example, in the ATARI game task,
the position (e.g., above/below/left/right) of a ball and thescore can be extracted as independent variables. If
independent factors are extracted, unexperienced states
could be inferred from their combinations.
Categorization
The ability to classify a continuous state space into a finite
discretized set. In the brain, nonparametric categorization(i.e., the number of categories are not determined) must be
performed.
Categorized elements can be associated with symbols andform the basis for symbolic concepts and symbols. They
also provide the discretization necessary for regarding thesystem as an automaton and give the foundation for action
selection. They are also essential to support negative
concepts and ontology [Yamakawa, 2014].
Sparse representation
Information representation for which only a small numberof variables have non-zero values while the value of the
majority is zero. In the neocortex, circuitry containing
inhibitory cells may implement k-WTA (k-winners take all)to realize sparse representation. In engineering,
regularization terms are often introduced. In a sparse
representation, categorization becomes easier, because thesubstantive dimensions decrease as the vicinity of each statenarrows, though the apparent dimensions may increase[Yamakawa, 2014].
Internal state
Internal states are essential for working memory required invarious cognitive functions to hold information in an
operable form. State holding is also indispensable forbinding information represented in disparate locations(distributed representation in neural circuits). It is presumed
to be realized with local recurrent circuits or fast synaptic
plasticity. The function of simply sustaining a state likeafterimage is referred to as "maintain simple state" (where
the state may not be discretized) in this paper.
A system that processes time series with discrete internal
states is called an automaton, categorized according to thetype of recognizable formal language. For instance, a finite
state machine recognizes regular grammars and a pushdown
automaton (PDA) recognizes a context-free grammar with anested structure. From the functional viewpoint, a system
can be regarded as an automaton without discrete internal
states as long as it handles a discrete time series. The
function of automatons is assumed in living creatures. For
instance, the songs of birds and whales can be represented
with regular grammar, and most of the grammar of humanlanguages can be described with context-free grammar. In
order for the brain to generate and recognize time series
with syntax, it must emulate the type of automaton that
corresponds to the grammar. Furthermore, the ability tohandle working memory can also be modeled as an
automaton.
Recurrent neural networks (RNN) have been proved to betheoretically equivalent to the Turing machine, capable ofemulating arbitrary automata [Siegelmann and Sontag,
1995]. In practical use, RNN is known to emulate finite
state machines and PDAs [Horne et al., 1998] [Gers andSchmidhuber, 2001]. RNNs with gates such as LSTM andGRU can be regarded as realizing a stack in PDA with theirstate-holding gate circuits. For example, LSTM recognizes
and generates nested tags in XML [Graves, 2013].
Learning modulation
In recent years, global projection of neural modulators has
also been suggested [Pi et al., 2013]. Such projections may
control learning coefficients and reinforcement learning in
the entire neocortex. Such a mechanism could be also
adopted in NMA.
Parallelism
Model parallelism is one of the practical requirements for
NMA to be scalable to real-world problems, eventually tothe scale comparable to the brain. Many artificial neuralnetwork algorithms make use of back-propagation in themanner through which the entire network is updated
synchronously. However, this synchronization bottleneckspoils the natural parallelism that biological neural circuits
hold. NMA will need to be equipped with the capability to
update its sub-modules concurrently and assign thesecomputational loads to different processing units.

3.2 Reviewing existing models
In this section, we examine existing models as candidates
for NMA. Table 1 compares their I/O and functions.
PredNet, HTM, and BESOM are inspired by brain
mechanisms and the stacked autoencoder, the laddernetwork, and CNN are based on engineering ideas.
Deep Predictive Coding Network (Deep PredNet)
Deep PredNet is a hierarchical neural network, with the
top-down signal representing prediction and the bottom-up
signal prediction error. It is developed by David Cox, et al.
[Lotter et al., 2016] based on the idea of predictive coding.
It performs unsupervised learning by prediction, combining
CNN and LSTM to represent intralaminar coupling.
Hierarchical Temporal Memory (HTM)
HTM is a machine learning system inspired by the structureof the neocortex and characterized by prediction, sparse
coding, and hierarchy (see [George and Hawkins, 2009] forneuroscientific interpretation and the white paper [Hawkins,
2010] for its implementation). While the multi-layer model
may not have been implemented, data clustering has been
implemented [Balasubramaniam et al., 2015].
BESOM
BESOM (BidirEctional Self-Organizing Map) is acomputational model of the neocortex that combines ahierarchical Bayesian network, self-organizing maps for
categorization, and an independent component analysis fororthogonalization [Ichisugi, 2007].

Stacked Autoencoder
A neural autoencoder functions as a dimension reducer,
using the input and output layers with the same number ofneurons and a hidden layer in-between with fewer neurons,
allowing the input to be compressed in the hidden layer andthe output to be decompressed. With a stacked autoencoder,end-to-end learning is often performed after pre-training
from the bottom layer.
Ladder Network
A semi-supervised learning model that combines anunsupervised autoencoder with a supervised learning modelthat performs well in classification, even with relatively
sparse training data [Rasmus et al., 2015].

methods. Procedia Computer Science, 60:1516 – 1523,
2015.
[Bastos et al., 2012] Andre M. Bastos, W. Martin Usrey,
Rick A. Adams, George R. Mangun, Pascal Fries, andKarl J. Friston. Canonical Microcircuits for Predictive
Coding. Neuron, 76(4):695–711, May 2012.
[Domingos, 2015] Pedro Domingos. The Master Algorithm:
How the Quest for the Ultimate Learning Machine Will
Remake Our World. Basic Books. Basic Books, 2015.
[Funamizu et al., 2016] Akihiro Funamizu, Bernd Kuhn,
and Kenji Doya. Neural substrate of dynamic Bayesianinference in the cerebral cortex. Nat Neurosci,
19(12):1682– 1689, Dec 2016.

Convolutional Neural Network (CNN)
A hierarchical neural network in which convolution layers
for dimension reduction and pooling layers for information
selection are alternately stacked, whose layers havedetectors called filters. Its convolution layer is analogous to
L2/3 and pooling layer to L4. It is mainly used for image
processing, for which neurons in a higher layer have wider
receptive fields. The input from the sensor is processedbottom-up and no loop exists in the network.

[George and Hawkins, 2009] Dileep George and Jeff
Hawkins. Towards a mathematical theory of cortical
micro-circuits. PLOS Computational Biology, 5(10):1–
26, 10 2009.

Canonical Microcircuits for Predictive Coding (CMPC)
Friston, et al., who have been formulating neural
information processing in terms of the free energy principle
and active inference, analyzed the structure of the neocortex
from the viewpoint of predictive coding [Bastos et al., 2012].

[Graves, 2013] A. Graves. Generating Sequences With Recurrent Neural Networks. arXiv e-prints, August 2013.

PBWM model
The PBWM (Prefrontal cortex and Basal ganglia Working
Memory) model proposed in [O’Reilly and Frank, 2006] hasa macro-circuit consisting of the neocortex, basal ganglia,
and thalamus, enabling operations that require themaintenance of states. The timing of the working memory
update is determined by reinforcement learning in the basal
ganglia.

4

Conclusion

In this paper, we proposed an NMA framework for general
intelligence corresponding to the canonical cortical circuit.
We described its I/O semantics and desirable functions. We
also evaluated candidate ANN models describing the
neocortical circuit and found that they neither model
attentional control nor temporal hidden states, even thoughsome of them model top-down and bottom-up
communication. In this, we recognize the need for theformulation and implementation of NMA with desirable
functions, toward which neuroscientists and AI researchers
should enhance collaboration.

References[Balasubramaniam et al., 2015] Jahan Balasubramaniamn,
C.B. Gokul Krishnaa, and Fangming Zhu. Enhancementof classifiers in htm-cla using similarity evaluation[Gers and Schmidhuber, 2001] F. A. Gers and E. Schmidhuber. LSTM recurrent networks learn simple context-freeand context-sensitive languages. IEEE Transactions on
Neural Networks, 12(6):1333–1340, Nov 2001.

[Harris and Mrsic-Flogel, 2013] Kenneth D. Harris andThomas D. Mrsic-Flogel. Cortical connectivity andsensory coding. Nature, 503(7474):51–58, Nov 2013.
[Hawkins, 2010] Donna Dubinsky Jeff Hawkins. Hierarchical temporal memory including HTM cortical learningalgorithms. Technical report, 2010.
[Horne et al., 1998] Bill G. Horne, C. Lee Giles, Pete C.
Collingwood, School Of Computing, Man Sci, Peter
Tino, and Peter Tino. Finite state machines and recurrent
neural networks – automata and dynamical systems
approaches. In Neural Networks and Pattern Recognition,
pages 171– 220. Academic Press, 1998.
[Ichisugi, 2007] Yuuji Ichisugi. A cerebral cortex modelthat self-organizes conditional probability tables andexecutes belief propagation. In 2007 International Joint
Conference on Neural Networks, pages 178–183, Aug
2007.
[Kowadlo and Rawlinson, 2015] Gideon Kowadlo andDavid Rawlinson. How to build a General Intelligence:
Circuits and Pathways. Project AGI (Blog), http://agi.io/.
2015.
[Kowadlo and Rawlinson, 2016] Gideon Kowadlo andDavid Rawlinson. How to build a General Intelligence:
An interpretation of the biology. Project AGI (Blog),
http://agi.io/, 2016.
[Lotter et al., 2016] William Lotter, Gabriel Kreiman, andDavid Cox. Deep predictive coding networks for video
prediction
andunsupervised
learning.
CoRR,
abs/1605.08104, 2016.

[Markman and Gentner, 2000] Arthur B. Markman and Dedre Gentner. Structure mapping in the comparison process. The American Journal of Psychology, 113(4):501–
538, 2000.
[O’Reilly and Frank, 2006] Randall C. O’Reilly andMichael J. Frank. Making working memory work: A
computational model of learning in the prefrontal cortex
and basal ganglia. Neural Comput., 18(2):283–328,
February 2006.
[Pi et al., 2013] Hyun-Jae Pi, Balazs Hangya, Duda Kvitsiani, Joshua I Sanders, Z Josh Huang, and Adam
Kepecs. Cortical interneurons that specialize in
disinhibitory con- trol. Nature, 503(7477):521–524, Nov
2013.
[Rao and Ballard, 1999] Rajesh P. N. Rao and Dana H. Ballard. Predictive coding in the visual cortex: a functional
interpretation of some extra-classical receptive-field
effects. Nat Neurosci, 2(1):79–87, jan 1999.
[Rasmus et al., 2015] Antti Rasmus, Harri Valpola, Mikko
Honkala, Mathias Berglund, and Tapani Raiko. Semisupervised learning with ladder network. CoRR,
abs/1507.02672, 2015.
[Siegelmann and Sontag, 1995] H.T. Siegelmann and E.D.
Sontag. On the computational power of neural nets.
Journal of Computer and System Sciences, 50(1):132 –
150, 1995.
[Solari and Stoner, 2011] Soren Van Hout Solari and Rich
Stoner. Cognitive Consilience: Primate Non-Primary
Neuroanatomical Circuits Underlying Cognition.
Frontiers in Neuroanatomy, 5:65, Dec 2011.
[Yamakawa, 2014] Hiroshi Yamakawa. Column Structureson the Neocortex as a Basis of Symbolic Representation.
JSAI 2014, 2C4-OS-22a-4, 2014. (In Japanese)